A basic oxygen furnace (BOF) is a batch chemical reactor that converts hot metal produced from a blast furnace to liquid steel with the desired steel grade, composition, weight, and temperature. To do so, high-purity oxygen is blown through a molten bath to lower the carbon, silicon, manganese, and phosphorous contents of the liquid iron. The impurities and a small amount of oxidized iron are then carried off in the molten slag that floats on the surface of the hot metal. After the steel has been refined, the molten steel is poured into a preheated ladle. Alloys are added to the ladle during this pouring operation in order to give the steel the precise composition desired—referred to as the “first turndown”.
The precise control of BOF first turndown quality and batch production times is an important factor in maximizing steel productivity and yield, reducing energy and material consumption, and balancing the prediction path from the BOF to the continuous casting operation. Decrease in both the mean and standard deviation of the error between target and actual quality is also economically desirable.
Presently known BOF systems are difficult to control. For example, there is typically almost no real-time information available during the BOF batch process, and thus the quality at the end of the batch depends entirely on the recipe at the starting point of the batch. Furthermore, the BOF process is very complex and highly nonlinear, and the performance of a static charge model can be unsatisfactory. Because of the lack of real-time quality information during the batch process, the prediction and control of first turndown quality at the charge is important in effectively controlling BOF production.
The function of a BOF predictor is to generate the prediction of the quality (i.e., major chemical compositions, temperature, and weight) at the first turndown with minimum prediction error between the actual and predicted quality. Most prior art prediction methods are based on a feedforward neural network (NN) with back propagation (BP) supervised learning. However, the EMR (empirical minimum risk) type neural network training can result in over-fitting of the data if the termination of NN training cannot be controlled properly. On the other hand, the generalization performance heavily relies on the training examples selected from the huge amount of historical batch data—namely, the inputs, the predicted quality, and the actual results at the first turndown batch after batch.
This control system can also be illustrated with the concept of data pattern mapping from the uncontrollable input space and aim quality (at the first turndown) space representing the target quality space to the output space, i.e., the charge decision space. From a computation point of view, such a complex system cannot provide a unique solution with consistency, particularly for noisy data. To improve the open loop control quality, it has been a secondary measure to predict the first turndown quality with uncontrollable inputs and calculated charge decisions in terms of machine learning and data mining techniques.
Accordingly, a need therefore exists for an on-line (networked) system that provides more precise prediction results and control decisions under variable production conditions and with noisy data at the time of charge operations. More particularly, such system should be capable of providing control decisions irrespective of batch data quality and controllability of input space. Other desirable features and characteristics of the present invention will become apparent from the subsequent detailed description of the invention and the appended claims, taken in conjunction with the accompanying drawings and this background of the invention.